International Journal Of Engineering Technology And Management Sciences

AUTHOR(S)

Animesh Samanta, Akash Chowdhury, Argha Ghosh, Sulekha Das, Dr. Avijit Kumar Chaudhuri

DOI: https://doi.org/10.46647/ijetms.2023.v07i02.009

ABSTRACT
The study of Homeless people is challenging, and describing Black Homelessness is especially difficult because of spotty record keeping[1]. The story of homeless Afro-Americans is often absent from the usual textbook study of homeless people. Homelessness is incompatibly experienced among historically marginalized groups in the united states, but for Black Americans, the distinction is especially stark[1]. Homeless condition is often occurred to the condition of poverty, less number of house, and less number of beds in the room. In most homes, there are fewer than three bedrooms and the income of each family is less, most of the rooms are already occupied. Some people in the Afro-American community are living their lives as renters what are the rent cost including the utilities like electricity, gas, water, etc is also very high. The communication system of the caucasian population is bad also, there is no phone in the family. The main reason for homelessness is that a huge number of people are immigrating within the last 5 years. The economic condition of Afro-Americans is at the poverty level, with few services, and often very poor educational opportunities. Here the data was analyzed using Multiple Regression Analysis (MRA). The proposed model is tested on the “Communities and Crime Data Set” from the UCI Machine Learning Repository: which is available at https://archive.ics.uci.edu/ml/datasets/communities+and+crime. Through this research, approximately 99.49 percent of the data was predicted correctly.

Page No: 66 - 74

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How to Cite This Article:
Animesh Samanta, Akash Chowdhury, Argha Ghosh, Sulekha Das, Dr. Avijit Kumar Chaudhuri . Early prediction of outcast people in the Afro-American community using machine learning technique . ijetms;7(2):66-74. DOI: 10.46647/ijetms.2023.v07i02.009